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findmywayfindmyway
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Add MCCFR (#90)
* add outcome_sampling_mccfr * add esmccfr * update README.md
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‎README.md

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@@ -25,7 +25,7 @@ This project aims to provide some implementations of the most typical reinforcem
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- DDPG
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- TD3
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- SAC
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- CFR
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- CFR/OS-MCCFR/ES-MCCFR
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- Minimax
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If you are looking for tabular reinforcement learning algorithms, you may refer [ReinforcementLearningAnIntroduction.jl](https://github.com/JuliaReinforcementLearning/ReinforcementLearningAnIntroduction.jl).

‎src/algorithms/cfr/cfr.jl

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include("tabular_cfr.jl")
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include("outcome_sampling_mccfr.jl")
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include("external_sampling_mccfr.jl")

‎src/algorithms/cfr/external_sampling_mccfr.jl

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export ExternalSamplingMCCFRPolicy
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using Random
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using StatsBase: sample, Weights
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"""
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ExternalSamplingMCCFRPolicy
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This implementation uses stochasticaly-weighted averaging.
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Ref:
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- [MONTE CARLO SAMPLING AND REGRET MINIMIZATION FOR EQUILIBRIUM COMPUTATION AND DECISION-MAKING IN LARGE EXTENSIVE FORM GAMES](http://mlanctot.info/files/papers/PhD_Thesis_MarcLanctot.pdf)
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- [Monte Carlo Sampling for Regret Minimization in Extensive Games](https://papers.nips.cc/paper/3713-monte-carlo-sampling-for-regret-minimization-in-extensive-games.pdf)
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"""
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struct ExternalSamplingMCCFRPolicy{S,T,R<:AbstractRNG} <: AbstractPolicy
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nodes::Dict{S,InfoStateNode}
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behavior_policy::QBasedPolicy{TabularLearner{S,T},WeightedExplorer{true,R}}
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end
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(p::ExternalSamplingMCCFRPolicy)(env::AbstractEnv) = p.behavior_policy(env)
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RLBase.get_prob(p::ExternalSamplingMCCFRPolicy, env::AbstractEnv) = get_prob(p.behavior_policy, env)
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function ExternalSamplingMCCFRPolicy(;
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env::AbstractEnv,
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n_iter::Int,
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rng=Random.GLOBAL_RNG,
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)
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@assert NumAgentStyle(env) isa MultiAgent
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@assert DynamicStyle(env) === SEQUENTIAL
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@assert RewardStyle(env) === TERMINAL_REWARD
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@assert ChanceStyle(env) === EXPLICIT_STOCHASTIC
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@assert DefaultStateStyle(env) === Information{String}()
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nodes = init_info_state_nodes(env)
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for i in 1:n_iter
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for p in get_players(env)
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if p != get_chance_player(env)
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external_sampling(copy(env), p, nodes, rng)
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end
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end
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end
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behavior_policy = QBasedPolicy(;
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learner = TabularLearner{String}(),
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explorer = WeightedExplorer(; is_normalized = true, rng = rng),
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)
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for (k, v) in nodes
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s = sum(v.cumulative_strategy)
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if s != 0
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update!(behavior_policy, k => v.cumulative_strategy ./ s)
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end
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end
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ExternalSamplingMCCFRPolicy(nodes, behavior_policy)
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end
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function external_sampling(env, i, nodes, rng)
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current_player = get_current_player(env)
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if get_terminal(env)
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get_reward(env, i)
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elseif current_player == get_chance_player(env)
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env(rand(rng, get_actions(env)))
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external_sampling(env, i, nodes, rng)
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else
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I = get_state(env)
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node = nodes[I]
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regret_matching!(node)
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σ, rI, sI = node.strategy, node.cumulative_regret, node.cumulative_strategy
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n = length(node.strategy)
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if i == current_player
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u = zeros(n)
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uσ = 0
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for (aᵢ, a) in enumerate(get_legal_actions(env))
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u[aᵢ] = external_sampling(child(env, a), i, nodes, rng)
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uσ += σ[aᵢ] * u[aᵢ]
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end
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rI .+= u .- uσ
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uσ
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else
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a′ = sample(rng, Weights(σ, 1.0))
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env(get_legal_actions(env)[a′])
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u = external_sampling(env, i, nodes, rng)
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sI .+= σ
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u
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end
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end
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end

‎src/algorithms/cfr/outcome_sampling_mccfr.jl

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export OutcomeSamplingMCCFRPolicy
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using Random
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using StatsBase: sample, Weights
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"""
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OutcomeSamplingMCCFRPolicy
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This implementation uses stochasticaly-weighted averaging.
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Ref:
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- [MONTE CARLO SAMPLING AND REGRET MINIMIZATION FOR EQUILIBRIUM COMPUTATION AND DECISION-MAKING IN LARGE EXTENSIVE FORM GAMES](http://mlanctot.info/files/papers/PhD_Thesis_MarcLanctot.pdf)
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- [Monte Carlo Sampling for Regret Minimization in Extensive Games](https://papers.nips.cc/paper/3713-monte-carlo-sampling-for-regret-minimization-in-extensive-games.pdf)
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"""
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struct OutcomeSamplingMCCFRPolicy{S,T,R<:AbstractRNG} <: AbstractPolicy
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nodes::Dict{S,InfoStateNode}
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behavior_policy::QBasedPolicy{TabularLearner{S,T},WeightedExplorer{true,R}}
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end
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(p::OutcomeSamplingMCCFRPolicy)(env::AbstractEnv) = p.behavior_policy(env)
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RLBase.get_prob(p::OutcomeSamplingMCCFRPolicy, env::AbstractEnv) = get_prob(p.behavior_policy, env)
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function OutcomeSamplingMCCFRPolicy(;
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env::AbstractEnv,
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n_iter::Int,
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rng=Random.GLOBAL_RNG,
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ϵ=0.6
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)
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@assert NumAgentStyle(env) isa MultiAgent
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@assert DynamicStyle(env) === SEQUENTIAL
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@assert RewardStyle(env) === TERMINAL_REWARD
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@assert ChanceStyle(env) === EXPLICIT_STOCHASTIC
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@assert DefaultStateStyle(env) === Information{String}()
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nodes = init_info_state_nodes(env)
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for i in 1:n_iter
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for p in get_players(env)
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if p != get_chance_player(env)
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outcome_sampling(copy(env), p, nodes, ϵ, 1.0, 1.0, 1.0, rng)
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end
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end
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end
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behavior_policy = QBasedPolicy(;
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learner = TabularLearner{String}(),
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explorer = WeightedExplorer(; is_normalized = true, rng = rng),
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)
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for (k, v) in nodes
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s = sum(v.cumulative_strategy)
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if s != 0
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update!(behavior_policy, k => v.cumulative_strategy ./ s)
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end
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end
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OutcomeSamplingMCCFRPolicy(nodes, behavior_policy)
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end
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function outcome_sampling(env, i, nodes, ϵ, πᵢ, π₋ᵢ, s, rng)
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current_player = get_current_player(env)
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if get_terminal(env)
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get_reward(env, i) / s, 1.0
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elseif current_player == get_chance_player(env)
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env(rand(rng, get_actions(env)))
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outcome_sampling(env, i, nodes, ϵ, πᵢ, π₋ᵢ, s, rng)
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else
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I = get_state(env)
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node = nodes[I]
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regret_matching!(node)
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σ, rI, sI = node.strategy, node.cumulative_regret, node.cumulative_strategy
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n = length(node.strategy)
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if i == current_player
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aᵢ = rand(rng) >= ϵ ? sample(rng, Weights(σ, 1.0)) : rand(rng, 1:n)
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pᵢ = σ[aᵢ] * (1 - ϵ) + ϵ / n
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πᵢ′, π₋ᵢ′, s′ = πᵢ * pᵢ, π₋ᵢ, s * pᵢ
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else
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aᵢ = sample(rng, Weights(σ, 1.0))
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pᵢ = σ[aᵢ]
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πᵢ′, π₋ᵢ′, s′ = πᵢ, π₋ᵢ * pᵢ, s * pᵢ
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end
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env(get_legal_actions(env)[aᵢ])
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u, πₜₐᵢₗ = outcome_sampling(env, i, nodes, ϵ, πᵢ′, π₋ᵢ′, s′, rng)
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if i == current_player
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w = u * π₋ᵢ
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rI .+= w * πₜₐᵢₗ .* ((1:n .== aᵢ) .- σ[aᵢ])
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else
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sI .+= π₋ᵢ / s .* σ
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end
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u, πₜₐᵢₗ * σ[aᵢ]
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end
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end

‎src/algorithms/cfr/tabular_cfr.jl

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@@ -144,6 +144,8 @@ function cfr!(nodes, env, player, reach_probs, chance_player_reach_prob, ratio)
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end
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end
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regret_matching!(node::InfoStateNode) = regret_matching!(node.strategy, node.cumulative_regret)
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function regret_matching!(strategy, cumulative_regret)
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s = mapreduce(x -> max(0, x), +, cumulative_regret)
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if s > 0
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function update_strategy!(nodes)
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for node in values(nodes)
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regret_matching!(node.strategy, node.cumulative_regret)
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regret_matching!(node)
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end
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end

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