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rollouts.jl
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rollouts.jl
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function lambda_rollout(T, rollout_length, context, use_context, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
#temp_post_covs = bandit_posterior_covs
#temp_post_means = bandit_posterior_means
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
for t in 1:min(T, rollout_length)
#context_seed = rand(1:context_dim)
#fill!(context, zero(context[1]))
#context[context_seed] = 1
if (t > 1) || !use_context
context = generate_context(context_dim, context_mean, context_sd, context_constant)
end
mul!(true_expected_rewards, bandit_param, context)
# alt action find
curr_ind = 1
curr_max = dot((@view bandit_posterior_means[1,:]),context) * (1-lambda[t]) + lambda[t] * dot(context, (@view bandit_posterior_covs[1,:,:]), context)
for i in 2:bandit_count
candidate_max = dot((@view bandit_posterior_means[i,:]),context) * (1-lambda[t]) + lambda[t] * dot(context, (@view bandit_posterior_covs[i,:,:]), context)
if candidate_max > curr_max
curr_ind = i
curr_max = candidate_max
end
end
action = curr_ind
#end
true_expected_reward = true_expected_rewards[action]
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * obs_sd + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
return disc_reward
end
function lambda_mean_rollout(T_remainder, rollout_length, context, use_context, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
#temp_post_covs = bandit_posterior_covs
#temp_post_means = bandit_posterior_means
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
for t in 1:min(T_remainder, rollout_length)
#context_seed = rand(1:context_dim)
#fill!(context, zero(context[1]))
#context[context_seed] = 1
if (t > 1) || !use_context
context = generate_context(context_dim, context_mean, context_sd, context_constant)
end
mul!(true_expected_rewards, bandit_param, context)
#context = randn(context_dim) * context_sd .+ context_mean
#mean_max_bandit = 1
#mean_max_val = dot((@view bandit_posterior_means[1,:]), context)
#
#var_max_bandit = 1
#var_max_val = dot(context, (@view bandit_posterior_covs[1,:,:]), context)
#
#for i = 2:bandit_count
#
#new_val = dot((@view bandit_posterior_means[i,:]),context)
#if new_val > mean_max_val
#mean_max_val = new_val
#mean_max_bandit = i
#end
#
#new_val = dot(context, (@view bandit_posterior_covs[i, :, :]), context)
#
#if new_val > var_max_val
#var_max_val = new_val
#var_max_bandit = i
#end
#end
#
#if mean_max_bandit == var_max_bandit
#
#action = mean_max_bandit
#
#else
# alt action find
curr_ind = 1
curr_max = dot((@view bandit_posterior_means[1,:]),context) * (1-lambda[t]) + lambda[t] * max(1,T - T_remainder + t - 1) * dot(context, (@view bandit_posterior_covs[1,:,:]), context)
for i in 2:bandit_count
candidate_max = dot((@view bandit_posterior_means[i,:]),context) * (1-lambda[t]) + lambda[t] * max(1,T - T_remainder + t - 1) * dot(context, (@view bandit_posterior_covs[i,:,:]), context)
if candidate_max > curr_max
curr_ind = i
curr_max = candidate_max
end
end
action = curr_ind
#end
true_expected_reward = true_expected_rewards[action]
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * obs_sd + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
return disc_reward
end
function greedy_rollout(T_remainder, rollout_length, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
context, true_expected_rewards, CovCon, old_cov, SigInvMu)
disc_reward = 0
fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
for t in 1:min(T_remainder, rollout_length)
#context_seed = rand(1:context_dim)
#fill!(context, zero(context[1]))
#context[context_seed] = 1
context = generate_context(context_dim, context_mean, context_sd, context_constant)
mul!(true_expected_rewards, bandit_param, context)
curr_ind = 1
curr_max = dot((@view bandit_posterior_means[1,:]),context)
for i in 2:bandit_count
candidate_max = dot((@view bandit_posterior_means[i,:]),context)
if candidate_max > curr_max
curr_ind = i
curr_max = candidate_max
end
end
action = curr_ind
#end
true_expected_reward = true_expected_rewards[action]
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * obs_sd + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
return disc_reward
end
function val_greedy_rollout(T_remainder, rollout_length, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
context, true_expected_rewards, CovCon, old_cov, SigInvMu)
disc_reward = 0
fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
truncation_length = T_remainder - min(T_remainder, rollout_length)
for t in 1:min(T_remainder, rollout_length)
context_seed = rand(1:context_dim)
fill!(context, zero(context[1]))
context[context_seed] = 1
mul!(true_expected_rewards, bandit_param, context)
context = generate_context(context_dim, context_mean, context_sd, context_constant)
curr_ind = 1
curr_max = dot((@view bandit_posterior_means[1,:]),context)
for i in 2:bandit_count
candidate_max = dot((@view bandit_posterior_means[i,:]),context)
if candidate_max > curr_max
curr_ind = i
curr_max = candidate_max
end
end
action = curr_ind
#end
# TRUE PARAM VERSION
#true_expected_reward = true_expected_rewards[action]
#disc_reward += true_expected_reward * discount^(t-1)
#obs = randn() * obs_sd + true_expected_reward
# UNKNOWN PARAM VERSION
true_expected_reward = dot(bandit_posterior_means[action,:], context)
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * sqrt(obs_sd^2 + dot(context, bandit_posterior_covs[action,:,:], context)) + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#print("Context: $(context)\n")
#print("Obs: $(obs)\n")
#print("Old Cov: $(old_cov)\n")
#print("Old Mean: $(bandit_posterior_means[action, :])\n")
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#Checking that computations are correct.
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#Checking that computations are correct
#print("New Cov: $(bandit_posterior_covs[action,:,:])\n")
#print("New Mean: $(bandit_posterior_means[action, :, :])\n")
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
if truncation_length > 0
#### SWITCHED TO TRUE VALUE NEURAL NETWORK AS TEST
#input_vec = Vector(vec(bandit_param'))
#append!(input_vec, Vector(vec(bandit_posterior_means')))
input_vec = Vector(vec(bandit_posterior_means'))
append!(input_vec, vcat([upper_triangular_vec(bandit_posterior_covs[a, :, :]) for a in 1:bandit_count]...))
scaled_input_vec = (input_vec .- scale_list[truncation_length][1:end .!= 1, 1]) ./ scale_list[truncation_length][1:end .!= 1, 2]
disc_reward += (discount^min(T_remainder, rollout_length)) * (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1])
# Evaluating parameters for sanity check
#test_out = []
#append!(test_out, input_vec)
#append!(test_out, scaled_input_vec)
#append!(test_out, (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1]))
#append!(test_out, neural_net_list[truncation_length](scaled_input_vec)[1])
#print(test_out)
end
return disc_reward
end
function val_epsilon_greedy_rollout(epsilon, T_remainder, rollout_length, context, use_context, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
truncation_length = T_remainder - min(T_remainder, rollout_length)
for t in 1:min(T_remainder, rollout_length)
if t > 1 || !use_context
context = generate_context(context_dim, context_mean, context_sd, context_constant)
end
mul!(true_expected_rewards, bandit_param, context)
if rand() > epsilon
curr_ind = 1
curr_max = dot((@view bandit_posterior_means[1,:]),context)
for i in 2:bandit_count
candidate_max = dot((@view bandit_posterior_means[i,:]),context)
if candidate_max > curr_max
curr_ind = i
curr_max = candidate_max
end
end
action = curr_ind
else
action = rand(1:bandit_count)
end
# TRUE PARAM VERSION
#true_expected_reward = true_expected_rewards[action]
#disc_reward += true_expected_reward * discount^(t-1)
#obs = randn() * obs_sd + true_expected_reward
# UNKNOWN PARAM VERSION
true_expected_reward = dot(bandit_posterior_means[action,:], context)
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * sqrt(obs_sd^2 + dot(context, bandit_posterior_covs[action,:,:], context)) + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#print("Context: $(context)\n")
#print("Obs: $(obs)\n")
#print("Old Cov: $(old_cov)\n")
#print("Old Mean: $(bandit_posterior_means[action, :])\n")
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#Checking that computations are correct.
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#Checking that computations are correct
#print("New Cov: $(bandit_posterior_covs[action,:,:])\n")
#print("New Mean: $(bandit_posterior_means[action, :, :])\n")
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
if truncation_length > 0
#### SWITCHED TO TRUE VALUE NEURAL NETWORK AS TEST
#input_vec = Vector(vec(bandit_param'))
#append!(input_vec, Vector(vec(bandit_posterior_means')))
input_vec = Vector(vec(bandit_posterior_means'))
append!(input_vec, vcat([upper_triangular_vec(bandit_posterior_covs[a, :, :]) for a in 1:bandit_count]...))
scaled_input_vec = (input_vec .- scale_list[truncation_length][1:end .!= 1, 1]) ./ scale_list[truncation_length][1:end .!= 1, 2]
disc_reward += (discount^min(T_remainder, rollout_length)) * (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1])
# Evaluating parameters for sanity check
#test_out = []
#append!(test_out, input_vec)
#append!(test_out, scaled_input_vec)
#append!(test_out, (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1]))
#append!(test_out, neural_net_list[truncation_length](scaled_input_vec)[1])
#print(test_out)
end
return disc_reward
end
function val_fractional_thompson_rollout(coarse, T_remainder, rollout_length, context, use_context, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
truncation_length = T_remainder - min(T_remainder, rollout_length)
for t in 1:min(T_remainder, rollout_length)
if t > 1 || !use_context
context = generate_context(context_dim, context_mean, context_sd, context_constant)
end
mul!(true_expected_rewards, bandit_param, context)
if coarse == 0
action = greedy_policy(t, T, bandit_count, context, bandit_posterior_means, bandit_posterior_covs, discount, epsilon, rollout_length, n_rollouts, n_opt_rollouts, context_dim)
else
frac = 1 / coarse - 1
action = thompson_policy(t, T, bandit_count, context, bandit_posterior_means, bandit_posterior_covs ./ frac, discount, epsilon, rollout_length, n_rollouts, n_opt_rollouts, context_dim)
end
# TRUE PARAM VERSION
#true_expected_reward = true_expected_rewards[action]
#disc_reward += true_expected_reward * discount^(t-1)
#obs = randn() * obs_sd + true_expected_reward
# UNKNOWN PARAM VERSION
true_expected_reward = dot(bandit_posterior_means[action,:], context)
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * sqrt(obs_sd^2 + dot(context, bandit_posterior_covs[action,:,:], context)) + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#print("Context: $(context)\n")
#print("Obs: $(obs)\n")
#print("Old Cov: $(old_cov)\n")
#print("Old Mean: $(bandit_posterior_means[action, :])\n")
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#Checking that computations are correct.
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#Checking that computations are correct
#print("New Cov: $(bandit_posterior_covs[action,:,:])\n")
#print("New Mean: $(bandit_posterior_means[action, :, :])\n")
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
if truncation_length > 0
#### SWITCHED TO TRUE VALUE NEURAL NETWORK AS TEST
#input_vec = Vector(vec(bandit_param'))
#append!(input_vec, Vector(vec(bandit_posterior_means')))
input_vec = Vector(vec(bandit_posterior_means'))
append!(input_vec, vcat([upper_triangular_vec(bandit_posterior_covs[a, :, :]) for a in 1:bandit_count]...))
scaled_input_vec = (input_vec .- scale_list[truncation_length][1:end .!= 1, 1]) ./ scale_list[truncation_length][1:end .!= 1, 2]
disc_reward += (discount^min(T_remainder, rollout_length)) * (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1])
# Evaluating parameters for sanity check
#test_out = []
#append!(test_out, input_vec)
#append!(test_out, scaled_input_vec)
#append!(test_out, (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1]))
#append!(test_out, neural_net_list[truncation_length](scaled_input_vec)[1])
#print(test_out)
end
return disc_reward
end
function val_rollout(ep, policy, T_remainder, rollout_length, context, use_context, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
truncation_length = T_remainder - min(T_remainder, rollout_length)
t_curr = T - T_remainder + 1
for t in 1:min(T_remainder, rollout_length)
#context_seed = rand(1:context_dim)
#fill!(context, zero(context[1]))
#context[context_seed] = 1
#mul!(true_expected_rewards, bandit_param, context)
if t > 1 || !use_context
context = generate_context(context_dim, context_mean, context_sd, context_constant)
end
action = policy(ep, t, T, bandit_count, context, bandit_posterior_means, bandit_posterior_covs, discount, epsilon, rollout_length, n_rollouts, n_opt_rollouts, context_dim)
#TRUE PARAM VERSION
mr = maximum(bandit_param * context)
true_expected_reward = dot(bandit_param[action, :], context)
disc_reward += (true_expected_reward-mr) * discount^(t-1)
obs = randn() * obs_sd + true_expected_reward
# UNKNOWN PARAM VERSION
#true_expected_reward = dot(bandit_posterior_means[action,:], context)
#disc_reward += true_expected_reward * discount^(t-1)
#obs = randn() * sqrt(obs_sd^2 + dot(context, bandit_posterior_covs[action,:,:], context)) + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#print("Context: $(context)\n")
#print("Obs: $(obs)\n")
#print("Old Cov: $(old_cov)\n")
#print("Old Mean: $(bandit_posterior_means[action, :])\n")
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#Checking that computations are correct.
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#Checking that computations are correct
#print("New Cov: $(bandit_posterior_covs[action,:,:])\n")
#print("New Mean: $(bandit_posterior_means[action, :, :])\n")
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
if truncation_length > 0
if true
t_trunc = t_curr + min(T_remainder, rollout_length)
reg_est = 0
for n in 1:2
context = generate_context(context_dim, context_mean, context_sd, context_constant)
context_reg_est = 0
expected_rewards = [dot(context, bandit_posterior_means[k, :]) for k in 1:bandit_count]
variance_rewards = [dot(context, bandit_posterior_covs[k, :, :], context) for k in 1:bandit_count]
#point_samps = expected_rewards .+ sqrt.(variance_rewards) .* randn(bandit_count, 500)
#expected_rewards = bandit_param * context
max_reward = maximum(expected_rewards)
if String(Symbol(policy)) == "thompson_policy"
for m in 1:500
action = policy(ep, t_trunc, T, bandit_count, context, bandit_posterior_means, bandit_posterior_covs, discount, epsilon, rollout_length, n_rollouts, n_opt_rollouts, context_dim)
reg_est *= ((n-1)*500 + m - 1) / ((n-1)*500 + m)
reg_est += (max_reward - expected_rewards[action]) / ((n-1)*500 + m)
end
else
action = policy(ep, t_trunc, T, bandit_count, context, bandit_posterior_means, bandit_posterior_covs, discount, epsilon, rollout_length, n_rollouts, n_opt_rollouts, context_dim)
reg_est += (max_reward - expected_rewards[action]) / 2
#for m in 1:500
# reg_est *= ((n-1)*500 + m - 1) / ((n-1)*500 + m)
# reg_est += (findmax(point_samps[:, m])[1] - point_samps[action, m]) / ((n-1)*500 + m)
#end
end
#reg_est *= (n-1) / n
#reg_est += (findmax(point_samps)[1] - point_samps[action]) / n
end
disc_reward -= .9 * discount^min(T_remainder, rollout_length) * sum(discount^(t - t_trunc) * reg_est * (1 + T - t) / (1 + T - t_trunc) for t in t_trunc:T)
else
#### SWITCHED TO TRUE VALUE NEURAL NETWORK AS TEST
#input_vec = Vector(vec(bandit_param'))
#append!(input_vec, Vector(vec(bandit_posterior_means')))
input_vec = Vector(vec(bandit_posterior_means'))
append!(input_vec, vcat([upper_triangular_vec(bandit_posterior_covs[a, :, :]) for a in 1:bandit_count]...))
scaled_input_vec = (input_vec .- scale_list[truncation_length][1:end .!= 1, 1]) ./ scale_list[truncation_length][1:end .!= 1, 2]
disc_reward += (discount^min(T_remainder, rollout_length)) * (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1])
# Evaluating parameters for sanity check
#test_out = []
#append!(test_out, input_vec)
#append!(test_out, scaled_input_vec)
#append!(test_out, (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1]))
#append!(test_out, neural_net_list[truncation_length](scaled_input_vec)[1])
#print(test_out)
end
end
return disc_reward
end
function rollout(policy, T_remainder, rollout_length, context, use_context, lambda, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
truncation_length = T_remainder - min(T_remainder, rollout_length)
for t in 1:min(T_remainder, rollout_length)
#context_seed = rand(1:context_dim)
#fill!(context, zero(context[1]))
#context[context_seed] = 1
#mul!(true_expected_rewards, bandit_param, context)
if t > 1 || !use_context
context = generate_context(context_dim, context_mean, context_sd, context_constant)
end
action = policy(t, min(T_remainder, rollout_length), bandit_count, context, bandit_posterior_means, bandit_posterior_covs, discount, epsilon, rollout_length, n_rollouts, n_opt_rollouts, context_dim)
# TRUE PARAM VERSION
#true_expected_reward = true_expected_rewards[action]
#disc_reward += true_expected_reward * discount^(t-1)
#obs = randn() * obs_sd + true_expected_reward
# UNKNOWN PARAM VERSION
true_expected_reward = dot(bandit_posterior_means[action,:], context)
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * sqrt(obs_sd^2 + dot(context, bandit_posterior_covs[action,:,:], context)) + true_expected_reward
#old_cov = temp_post_covs[action, :, :]
old_cov .= bandit_posterior_covs[action, :, :]
#print("Context: $(context)\n")
#print("Obs: $(obs)\n")
#print("Old Cov: $(old_cov)\n")
#print("Old Mean: $(bandit_posterior_means[action, :])\n")
#CovCon .= old_cov * context ./ obs_sd
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
#bandit_posterior_covs[action, :, :] = inv(context * context' / obs_sd^2 + old_precision)
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
#Checking that computations are correct.
#bandit_posterior_covs[action, :, :] .= old_cov .- CovCon .* CovCon' ./ (1 + dot(context, old_cov, context))
#bandit_posterior_covs[action, :, :] .= ((bandit_posterior_covs[action, :, :]) + (bandit_posterior_covs[action, :, :])') ./ 2
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
#Checking that computations are correct
#print("New Cov: $(bandit_posterior_covs[action,:,:])\n")
#print("New Mean: $(bandit_posterior_means[action, :, :])\n")
#bandit_posterior_means[action, :] .= (bandit_posterior_covs[action, :, :]) * (old_cov \ (bandit_posterior_means[action,:]) + context * obs / obs_sd^2)
end
if truncation_length > 0
#### SWITCHED TO TRUE VALUE NEURAL NETWORK AS TEST
#input_vec = Vector(vec(bandit_param'))
#append!(input_vec, Vector(vec(bandit_posterior_means')))
# BEGIN REAL
#input_vec = Vector(vec(bandit_posterior_means'))
#append!(input_vec, vcat([upper_triangular_vec(bandit_posterior_covs[a, :, :]) for a in 1:bandit_count]...))
#scaled_input_vec = (input_vec .- scale_list[truncation_length][1:end .!= 1, 1]) ./ scale_list[truncation_length][1:end .!= 1, 2]
#disc_reward += (discount^min(T_remainder, rollout_length)) * (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1])
# END REAL
# Evaluating parameters for sanity check
#test_out = []
#append!(test_out, input_vec)
#append!(test_out, scaled_input_vec)
#append!(test_out, (scale_list[truncation_length][1,2]*neural_net_list[truncation_length](scaled_input_vec)[1] + scale_list[truncation_length][1,1]))
#append!(test_out, neural_net_list[truncation_length](scaled_input_vec)[1])
#print(test_out)
end
return disc_reward
end
function better_rollout(T, curr_t, rollout_length, context, use_context, lambda_param, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
#temp_post_covs = bandit_posterior_covs
#temp_post_means = bandit_posterior_means
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
for t in 1:min(T, rollout_length)
#context_seed = rand(1:context_dim)
#fill!(context, zero(context[1]))
#context[context_seed] = 1
if (t > 1) || !use_context
context = generate_context(context_dim, context_mean, context_sd, context_constant)
end
mul!(true_expected_rewards, bandit_param, context)
#context = randn(context_dim) * context_sd .+ context_mean
curr_ind = 1
curr_max = dot((@view bandit_posterior_means[1,:]),context) + lambda_param[1]*(log(t+curr_t)^(1+lambda_param[2])) * (1 - ((t+curr_t) / (curr_t+T))^lambda_param[3]) * dot(context, (@view bandit_posterior_covs[1,:,:]), context)
for i in 2:bandit_count
candidate_max = dot((@view bandit_posterior_means[i,:]),context) + lambda_param[1]*(log(t+curr_t)^(1+lambda_param[2])) * (1 - ((t+curr_t) / (curr_t+T))^lambda_param[3]) * dot(context, (@view bandit_posterior_covs[i,:,:]), context)
if candidate_max > curr_max
curr_ind = i
curr_max = candidate_max
end
end
action = curr_ind
# TRUE PARAM VERSION
#true_expected_reward = true_expected_rewards[action]
#disc_reward += true_expected_reward * discount^(t-1)
#obs = randn() * obs_sd + true_expected_reward
# UNKNOWN PARAM VERSION
true_expected_reward = dot(bandit_posterior_means[action,:], context)
disc_reward += true_expected_reward * discount^(t-1)
obs = randn() * sqrt(obs_sd^2 + dot(context, bandit_posterior_covs[action,:,:], context)) + true_expected_reward
old_cov .= bandit_posterior_covs[action, :, :]
mul!(CovCon, old_cov, context)
CovCon ./= obs_sd
dividy = 1 + dot(context ./ obs_sd, old_cov, context ./ obs_sd)
for i in 1:context_dim
for j in 1:context_dim
bandit_posterior_covs[action,j,i] = old_cov[j,i] - CovCon[j]*CovCon[i] / dividy
end
end
for i in 1:context_dim
for j in 1:(i-1)
bandit_posterior_covs[action,i,j] = (bandit_posterior_covs[action,i,j]+bandit_posterior_covs[action,j,i])/2
bandit_posterior_covs[action,j,i] = bandit_posterior_covs[action,i,j]
end
end
SigInvMu .= old_cov \ @view bandit_posterior_means[action,:]
SigInvMu .+= context .* obs ./ obs_sd.^2
mul!((@view bandit_posterior_means[action,:]), (@view bandit_posterior_covs[action,:,:]), SigInvMu)
end
return disc_reward
end
function val_better_rollout(T_remainder, curr_t, rollout_length, context, use_context, lambda_param, context_dim, context_mean,
context_sd, obs_sd, bandit_count, discount, bandit_posterior_covs, bandit_posterior_means, bandit_param,
true_expected_rewards, CovCon, old_cov, SigInvMu)
#temp_post_covs = bandit_posterior_covs
#temp_post_means = bandit_posterior_means
disc_reward = 0
#fill!(context, 0.0)
fill!(true_expected_rewards, 0.0)
fill!(CovCon, 0.0)
fill!(old_cov,0.0)
fill!(SigInvMu,0.0)
truncation_length = T_remainder - min(T_remainder, rollout_length)
for t in 1:min(T_remainder, rollout_length)
#context_seed = rand(1:context_dim)